{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": ""
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "数据集加载与预处理",
   "id": "8687cb23d3c4464c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-16T03:15:58.186344Z",
     "start_time": "2024-10-16T03:15:54.560140Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "# CIFAR-10 数据集加载与预处理\n",
    "transform = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])\n",
    "\n",
    "# 加载 CIFAR-10 训练集和测试集\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=256, shuffle=True, num_workers=4)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=256, shuffle=False, num_workers=4)\n",
    "\n",
    "# CIFAR-10 分类标签\n",
    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n"
   ],
   "id": "1ece708784b10ec3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "2. 模型1：5个卷积层和1个池化层",
   "id": "60addc85b281e8f8"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "\n",
   "id": "2ec13d980c8de7b9"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-16T03:16:03.240150Z",
     "start_time": "2024-10-16T03:16:03.235150Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SimpleCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n",
    "        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)\n",
    "        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)  # 池化层\n",
    "        self.fc1 = nn.Linear(256 * 16 * 16, 512)  # 修改后的展平尺寸\n",
    "        self.fc2 = nn.Linear(512, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.bn1 = nn.BatchNorm2d(32)\n",
    "        self.bn2 = nn.BatchNorm2d(64)\n",
    "        self.bn3 = nn.BatchNorm2d(128)\n",
    "        self.bn4 = nn.BatchNorm2d(128)\n",
    "        self.bn5 = nn.BatchNorm2d(256)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(self.relu(self.bn1(self.conv1(x))))\n",
    "        x = self.relu(self.bn2(self.conv2(x)))\n",
    "        x = self.relu(self.bn3(self.conv3(x)))\n",
    "        x = self.relu(self.bn4(self.conv4(x)))\n",
    "        x = self.relu(self.bn5(self.conv5(x)))\n",
    "\n",
    "        # 展平处理\n",
    "        x = x.view(x.size(0), -1)\n",
    "        \n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n"
   ],
   "id": "24300acbfb43362e",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "3. 模型2：10个卷积层和1个池化层",
   "id": "20eac3c1d665682f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-16T03:16:05.588466Z",
     "start_time": "2024-10-16T03:16:05.570467Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class DeepCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(DeepCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n",
    "        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)\n",
    "        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n",
    "        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)\n",
    "        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)\n",
    "        self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)\n",
    "        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n",
    "        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)  # 池化层\n",
    "        self.fc1 = nn.Linear(512 * 4 * 4, 1024)\n",
    "        self.fc2 = nn.Linear(1024, 512)\n",
    "        self.fc3 = nn.Linear(512, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.bn1 = nn.BatchNorm2d(32)\n",
    "        self.bn2 = nn.BatchNorm2d(64)\n",
    "        self.bn3 = nn.BatchNorm2d(128)\n",
    "        self.bn4 = nn.BatchNorm2d(256)\n",
    "        self.bn5 = nn.BatchNorm2d(512)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.relu(self.bn1(self.conv1(x)))\n",
    "        x = self.pool(self.relu(self.bn2(self.conv2(x))))\n",
    "        x = self.relu(self.bn3(self.conv3(x)))\n",
    "        x = self.relu(self.bn3(self.conv4(x)))\n",
    "        x = self.relu(self.bn4(self.conv5(x)))\n",
    "        x = self.pool(self.relu(self.bn4(self.conv6(x))))\n",
    "        x = self.relu(self.bn4(self.conv7(x)))\n",
    "        x = self.relu(self.bn5(self.conv8(x)))\n",
    "        x = self.relu(self.bn5(self.conv9(x)))\n",
    "        x = self.pool(self.relu(self.bn5(self.conv10(x))))\n",
    "        \n",
    "        # 修正后的展平处理\n",
    "        x = x.view(x.size(0), -1)\n",
    "        \n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n"
   ],
   "id": "cbe193a1c3423599",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "4. 模型训练与测试",
   "id": "3c5695f88beb1443"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-16T03:16:07.990267Z",
     "start_time": "2024-10-16T03:16:07.983266Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def train_model(model, trainloader, criterion, optimizer, device):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for inputs, labels in trainloader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "    return running_loss / len(trainloader)\n",
    "\n",
    "def test_model(model, testloader, criterion, device):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    test_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in testloader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            test_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "    accuracy = 100 * correct / total\n",
    "    return test_loss / len(testloader), accuracy\n"
   ],
   "id": "a54db6cad2ce93a1",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "5. 训练与性能对比",
   "id": "ca5052c6a5e80f8f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-16T03:36:10.366760Z",
     "start_time": "2024-10-16T03:16:10.635005Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# 实例化模型\n",
    "model1 = SimpleCNN().to(device)\n",
    "model2 = DeepCNN().to(device)\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer1 = optim.Adam(model1.parameters(), lr=0.001)\n",
    "optimizer2 = optim.Adam(model2.parameters(), lr=0.001)\n",
    "\n",
    "\n",
    "\n",
    "# 初始化用于保存训练结果的列表\n",
    "train_loss_history1, test_loss_history1, accuracy_history1 = [], [], []\n",
    "train_loss_history2, test_loss_history2, accuracy_history2 = [], [], []\n",
    "\n",
    "# 训练和测试模型1\n",
    "for epoch in range(20):\n",
    "    train_loss = train_model(model1, trainloader, criterion, optimizer1, device)\n",
    "    test_loss, accuracy = test_model(model1, testloader, criterion, device)\n",
    "    \n",
    "    # 保存每轮的损失和准确率\n",
    "    train_loss_history1.append(train_loss)\n",
    "    test_loss_history1.append(test_loss)\n",
    "    accuracy_history1.append(accuracy)\n",
    "    \n",
    "    print(f\"Model 1 - Epoch {epoch+1}: Train Loss = {train_loss:.4f}, Test Loss = {test_loss:.4f}, Accuracy = {accuracy:.2f}%\")\n",
    "\n",
    "# 训练和测试模型2\n",
    "for epoch in range(20):\n",
    "    train_loss = train_model(model2, trainloader, criterion, optimizer2, device)\n",
    "    test_loss, accuracy = test_model(model2, testloader, criterion, device)\n",
    "    \n",
    "    # 保存每轮的损失和准确率\n",
    "    train_loss_history2.append(train_loss)\n",
    "    test_loss_history2.append(test_loss)\n",
    "    accuracy_history2.append(accuracy)\n",
    "    \n",
    "    print(f\"Model 2 - Epoch {epoch+1}: Train Loss = {train_loss:.4f}, Test Loss = {test_loss:.4f}, Accuracy = {accuracy:.2f}%\")\n",
    "\n"
   ],
   "id": "fe20bf0fb9adaea",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1 - Epoch 1: Train Loss = 2.7186, Test Loss = 1.5347, Accuracy = 43.19%\n",
      "Model 1 - Epoch 2: Train Loss = 1.3976, Test Loss = 1.3760, Accuracy = 50.93%\n",
      "Model 1 - Epoch 3: Train Loss = 1.1822, Test Loss = 1.2515, Accuracy = 56.20%\n",
      "Model 1 - Epoch 4: Train Loss = 1.0098, Test Loss = 1.0702, Accuracy = 63.33%\n",
      "Model 1 - Epoch 5: Train Loss = 0.8975, Test Loss = 0.9962, Accuracy = 64.90%\n",
      "Model 1 - Epoch 6: Train Loss = 0.8080, Test Loss = 0.9105, Accuracy = 68.49%\n",
      "Model 1 - Epoch 7: Train Loss = 0.7432, Test Loss = 0.8200, Accuracy = 71.23%\n",
      "Model 1 - Epoch 8: Train Loss = 0.6877, Test Loss = 0.8149, Accuracy = 71.92%\n",
      "Model 1 - Epoch 9: Train Loss = 0.6437, Test Loss = 0.7479, Accuracy = 74.54%\n",
      "Model 1 - Epoch 10: Train Loss = 0.6043, Test Loss = 0.6825, Accuracy = 76.39%\n",
      "Model 1 - Epoch 11: Train Loss = 0.5758, Test Loss = 0.6888, Accuracy = 76.46%\n",
      "Model 1 - Epoch 12: Train Loss = 0.5490, Test Loss = 0.7082, Accuracy = 76.00%\n",
      "Model 1 - Epoch 13: Train Loss = 0.5214, Test Loss = 0.6161, Accuracy = 79.48%\n",
      "Model 1 - Epoch 14: Train Loss = 0.5014, Test Loss = 0.6302, Accuracy = 79.49%\n",
      "Model 1 - Epoch 15: Train Loss = 0.4902, Test Loss = 0.6528, Accuracy = 78.16%\n",
      "Model 1 - Epoch 16: Train Loss = 0.4647, Test Loss = 0.5948, Accuracy = 79.96%\n",
      "Model 1 - Epoch 17: Train Loss = 0.4419, Test Loss = 0.5276, Accuracy = 82.07%\n",
      "Model 1 - Epoch 18: Train Loss = 0.4300, Test Loss = 0.5616, Accuracy = 81.08%\n",
      "Model 1 - Epoch 19: Train Loss = 0.4135, Test Loss = 0.5485, Accuracy = 81.39%\n",
      "Model 1 - Epoch 20: Train Loss = 0.4018, Test Loss = 0.6419, Accuracy = 79.11%\n",
      "Model 2 - Epoch 1: Train Loss = 2.0275, Test Loss = 2.2023, Accuracy = 14.53%\n",
      "Model 2 - Epoch 2: Train Loss = 1.5701, Test Loss = 1.8385, Accuracy = 34.44%\n",
      "Model 2 - Epoch 3: Train Loss = 1.2611, Test Loss = 2.0657, Accuracy = 25.30%\n",
      "Model 2 - Epoch 4: Train Loss = 1.0417, Test Loss = 2.3662, Accuracy = 24.18%\n",
      "Model 2 - Epoch 5: Train Loss = 0.8904, Test Loss = 3.0971, Accuracy = 22.71%\n",
      "Model 2 - Epoch 6: Train Loss = 0.7870, Test Loss = 5.2004, Accuracy = 17.11%\n",
      "Model 2 - Epoch 7: Train Loss = 0.7090, Test Loss = 3.0409, Accuracy = 23.27%\n",
      "Model 2 - Epoch 8: Train Loss = 0.6303, Test Loss = 7.2005, Accuracy = 13.82%\n",
      "Model 2 - Epoch 9: Train Loss = 0.5721, Test Loss = 5.4501, Accuracy = 11.78%\n",
      "Model 2 - Epoch 10: Train Loss = 0.5284, Test Loss = 5.8790, Accuracy = 10.60%\n",
      "Model 2 - Epoch 11: Train Loss = 0.4824, Test Loss = 4.2221, Accuracy = 12.93%\n",
      "Model 2 - Epoch 12: Train Loss = 0.4533, Test Loss = 3.5949, Accuracy = 12.03%\n",
      "Model 2 - Epoch 13: Train Loss = 0.4183, Test Loss = 4.7171, Accuracy = 11.25%\n",
      "Model 2 - Epoch 14: Train Loss = 0.3935, Test Loss = 3.1121, Accuracy = 14.70%\n",
      "Model 2 - Epoch 15: Train Loss = 0.3705, Test Loss = 3.0513, Accuracy = 15.55%\n",
      "Model 2 - Epoch 16: Train Loss = 0.3394, Test Loss = 5.4655, Accuracy = 10.90%\n",
      "Model 2 - Epoch 17: Train Loss = 0.3176, Test Loss = 3.1223, Accuracy = 12.53%\n",
      "Model 2 - Epoch 18: Train Loss = 0.3004, Test Loss = 2.8285, Accuracy = 14.73%\n",
      "Model 2 - Epoch 19: Train Loss = 0.2854, Test Loss = 2.6156, Accuracy = 17.03%\n",
      "Model 2 - Epoch 20: Train Loss = 0.2670, Test Loss = 2.5887, Accuracy = 20.73%\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-16T03:36:21.860582Z",
     "start_time": "2024-10-16T03:36:21.517476Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 绘制训练和测试损失的对比图\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 模型1和模型2的训练损失对比\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_loss_history1, label='Model 1 Train Loss', color='blue')\n",
    "plt.plot(test_loss_history1, label='Model 1 Test Loss', color='lightblue')\n",
    "plt.plot(train_loss_history2, label='Model 2 Train Loss', color='red')\n",
    "plt.plot(test_loss_history2, label='Model 2 Test Loss', color='orange')\n",
    "plt.title('Train and Test Loss Comparison')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# 模型1和模型2的测试准确率对比\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(accuracy_history1, label='Model 1 Accuracy', color='blue')\n",
    "plt.plot(accuracy_history2, label='Model 2 Accuracy', color='red')\n",
    "plt.title('Test Accuracy Comparison')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "41a9b63fbc54478a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
